import torch.nn as nn
import torch.nn.functional as F

from model import MODEL


@MODEL.register_module()
class MultiScaleDiscriminator(nn.Module):
    def __init__(self, num_scale, discriminator_cfg, down_sample_method="avg"):
        super().__init__()
        assert down_sample_method in ["avg", "bilinear"]
        self.down_sample_method = down_sample_method

        self.discriminator_list = nn.ModuleList([
            MODEL.build_with(discriminator_cfg) for _ in range(num_scale)
        ])

    def down_sample(self, x):
        if self.down_sample_method == "avg":
            return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False)
        if self.down_sample_method == "bilinear":
            return F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=True)

    def forward(self, x):
        results = []
        for discriminator in self.discriminator_list:
            results.append(discriminator(x))
            x = self.down_sample(x)
        return results